Transcript

1.
Simultaneous Transcriptional Profiling of Bacteria and
Their Host Cells
Michael S. Humphrys1, Todd Creasy1, Yezhou Sun1, Amol C. Shetty1, Marcus C. Chibucos1,2,
Elliott F. Drabek1, Claire M. Fraser1,2, Umar Farooq1, Naomi Sengamalay1, Sandy Ott1, Huizhong Shou2,
Patrik M. Bavoil2,3, Anup Mahurkar1, Garry S. A. Myers1,2,3*
1 Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, 2 Department of Microbiology & Immunology,
University of Maryland School of Medicine, Baltimore, Maryland, United States of America, 3 Department of Microbial Pathogenesis, University of Maryland Dental School,
Baltimore, Maryland, United States of America
Abstract
We developed an RNA-Seq-based method to simultaneously capture prokaryotic and eukaryotic expression profiles of cells
infected with intracellular bacteria. As proof of principle, this method was applied to Chlamydia trachomatis-infected
epithelial cell monolayers in vitro, successfully obtaining transcriptomes of both C. trachomatis and the host cells at 1 and
24 hours post-infection. Chlamydiae are obligate intracellular bacterial pathogens that cause a range of mammalian
diseases. In humans chlamydiae are responsible for the most common sexually transmitted bacterial infections and
trachoma (infectious blindness). Disease arises by adverse host inflammatory reactions that induce tissue damage &
scarring. However, little is known about the mechanisms underlying these outcomes. Chlamydia are genetically intractable
as replication outside of the host cell is not yet possible and there are no practical tools for routine genetic manipulation,
making genome-scale approaches critical. The early timeframe of infection is poorly understood and the host transcriptional
response to chlamydial infection is not well defined. Our simultaneous RNA-Seq method was applied to a simplified in vitro
model of chlamydial infection. We discovered a possible chlamydial strategy for early iron acquisition, putative immune
dampening effects of chlamydial infection on the host cell, and present a hypothesis for Chlamydia-induced fibrotic scarring
through runaway positive feedback loops. In general, simultaneous RNA-Seq helps to reveal the complex interplay between
invading bacterial pathogens and their host mammalian cells and is immediately applicable to any bacteria/host cell
interaction.
Citation: Humphrys MS, Creasy T, Sun Y, Shetty AC, Chibucos MC, et al. (2013) Simultaneous Transcriptional Profiling of Bacteria and Their Host Cells. PLoS
ONE 8(12): e80597. doi:10.1371/journal.pone.0080597
Editor: Kyle Ramsey, Midwestern University, United States of America
Received September 18, 2013; Accepted October 14, 2013; Published December 4, 2013
Copyright: ß 2013 Humphrys et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Sequence data acquisition and analysis was funded in part by federal funds from the National Institute of Allergy and Infectious Diseases (NIAID),
National Institutes of Health, United States Department of Health and Human Services under contract number HHSN272200900007C, and by University of
Maryland School of Medicine startup funds to GM. Additional support was provided by U19AI084044 (NIAID) to PB, HS and GM. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: gmyers@som.umaryland.edu
The few studies that examine both bacterial and host cell
transcriptional responses separate the prokaryotic and eukaryotic
messenger RNA (mRNA) prior to microarray profiling (for
example [5–7]). Sufficient prokaryotic mRNA for hybridization
can be difficult to obtain unless axenic culture or selective
amplification [8] is used or, in the case of intracellular bacteria, in
vitro infections are established with high multiplicities of infection
(MOI). High MOIs may not represent natural infection levels,
distorting expression profiles. The early events following invasion
are often poorly characterized, as the small number of organisms
yields insufficient transcripts for microarray detection. Furthermore, standard microarrays are restricted to existing genome
annotation [1] and cannot detect novel RNA moieties that are not
printed on the array. Tiling arrays overcome this limitation and
have been successfully applied to bacteria, revealing antisense
RNA expression and other non-coding RNA (ncRNA) transcripts
[9–13]. However, the large size of eukaryotic genomes makes tiling
arrays [14] prohibitively expensive for host gene expression
studies. Tag-based sequencing methods [15] alleviate these
Introduction
Bacterial pathogens subvert host eukaryotic cellular pathways
for survival and replication; in turn, infected host cells respond to
the invading pathogen through cascading changes in gene
expression. Deciphering these complex temporal and spatial
dynamics to identify novel bacterial virulence factors or host
response pathways is crucial for improved diagnostics and
therapeutics. Microarrays have been the predominant methodology for determining gene expression profiles [1], revealing a
diversity of bacterial pathogenic mechanisms [2] and commonalities of the complex global host response to infection [3]. However,
microarrays are inadequate for profiling both prokaryotic and
eukaryotic RNA from infected cells, as they are limited to what
can be printed and detected on the array. Technical limitations
such as high background signals and cross-hybridization also limits
their dynamic range [4]. Consequently, array analyses of hostpathogen interactions have typically examined either the pathogen
or the host, but usually not both simultaneously.
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RNA-Seq Profiling of Bacteria-Infected Cells
(reviewed by [35]). A series of elegant microarray profiling
experiments outlined the chlamydial transcriptional landscape
over the course of in vitro infection [36–38], and in response to
various perturbations [37,39–42]. These analyses show substantial chlamydial gene expression by 6–8 hpi, continuing through
to a maximum by 24 hpi [36,38] when most genes are expressed
[36,38].
The critical early (1 to 3 hpi) and immediate-early (,1 hpi)
periods of C. trachomatis infection have not been comprehensively
characterized by these high throughput approaches, with only one
study examining the 1 hpi period [36]. As only a small number of
infecting organisms are present, the limitations inherent to
microarrays prevent accurate early transcript detection. Belland
et al [36] found twenty-nine C. trachomatis D genes detectably
expressed at 1 hpi in HeLa 229 cells, but only by using an MOI of
100. RNA-Seq on purified EBs and RBs of C. trachomatis [43] and
C. pneumoniae [44] allowed transcriptional start site mapping and
the identification of novel chlamydial ncRNAs at mid- and late
periods in the developmental cycle, but not earlier than 24 hpi.
The host cell transcriptional response to Chlamydia infection has
been studied using a variety of host cell types, patient tissues,
chlamydial strains and times post infection using arrayed subsets of
human genes [45–54]. The varying genes and the different
methodologies and strains used limits their comparative utility.
Generally, up-regulation of host genes involved in cytokine
expression, inflammation, signal transduction and innate immunity, and down-regulation of genes involved in metabolism and
cell cycle regulation was observed. The earliest time of C.
trachomatis infection was 2 hpi [49,54], with few differentially
expressed host genes (,20); the early host response to C. trachomatis
infection was subsequently described as ‘‘quiescent’’ [54]. RNASeq has not been applied to Chlamydia-infected host cells.
In this study, we employed simultaneous depletion of both
Chlamydia and human rRNA by affinity-based counter selection
[55] to enrich prokaryotic and eukaryotic RNA from infected cells.
Deep sequencing of these enriched fractions captured both
chlamydial and human transcriptomes from infected cells at the
immediate-early and mid- periods of in vitro infection, providing
proof-of-principle of the simultaneous RNA-Seq approach. In
addition, this validated data provides novel insights into chlamydial biology and the host epithelial cell response in vitro.
problems to some extent, allowing individual transcripts to be
digitally counted with a broad dynamic range. Nevertheless, as
these approaches only sample a small region of a transcript, they
cannot capture the full diversity of RNA classes and isoforms.
RNA-Seq, or deep sequencing of cDNA libraries by nextgeneration sequencing, circumvents many of the problems
associated with microarray profiling or tag-based sequencing.
RNA-Seq can comprehensively and systematically define the
transcriptome of an organism with minimal bias [1,16–18], across
different experimental conditions or cell types [17,18] without
probe design or cross-hybridization problems. RNA-Seq data are
consistent with microarray results [19–24] but are more sensitive,
with essentially an infinite dynamic range. RNA-Seq is annotationindependent [18], allowing novel transcript discovery without
being reliant on array design or preexisting annotation. Unlike tag
sequencing, RNA-Seq can distinguish different mRNA isoforms
and ncRNA, and can identify splice junctions and transcript
boundaries [25,26].
Despite these advantages, RNA-Seq profiling of both prokaryotic and eukaryotic gene expression from bacteria-infected cells is
technically challenging. Total RNA extracted from infected cells is
a heterogeneous mixture of many host and bacterial RNA
moieties. Ribosomal RNA (rRNA) is the most abundant,
representing up to 98% of total RNA [27]; however several
RNA classes are now recognized, encompassing diverse sizes with
many functions that remain to be elucidated [28]. Bacterial
mRNA is typically a minor fraction of an infected cell, even under
optimized in vitro conditions, and especially in early infection
periods where bacterial numbers can be low. In contrast to
eukaryotic mRNA, prokaryotic mRNA are often polycistronic and
typically lack a polyadenylated tail, which precludes hybridization
capture, cDNA synthesis or amplification using poly(T) oligomers.
Thus, any analysis strategy that examines the polyadenylated
eukaryotic fraction alone will not recover the full diversity of RNA
in an infected cell, missing bacterial mRNA, bacterial ncRNA and
eukaryotic ncRNA.
Members of the genus Chlamydia are obligate intracellular
bacteria that cause the most common human sexually transmitted bacterial infections and a range of mammalian diseases with
inflammatory etiologies. Infection is frequently asymptomatic and
is an outcome of a complex dialogue between the host and
Chlamydia [29]. In humans, disease sequelae results from longterm infections or re-infections that induce tissue damage and
scarring [30].
Chlamydia has a unique biphasic developmental cycle that
alternates between distinct forms. The infectious elementary body
(EB) enters the host cell and sequesters within a modified
membrane-bound inclusion where it decondenses into the noninfectious replicating form, the reticulate body (RB). From within
this unique compartment, chlamydiae exploit the host cell by
hijacking host organelles and metabolites [29]. Following replication, RBs differentiate back into infectious EBs, which are
dispersed following cell lysis. Interconversion occurs asynchronously; by later infection times, chlamydial inclusions contain a
variety of EBs, RBs and intermediate forms at various developmental stages. A reversible stress-response state, characterized by
morphologically aberrant, non-infectious forms, can be induced in
vitro by addition of stressors such as cytokines, antibiotics or by
nutrient restriction (reviewed by [31]).
Chlamydia remains intractable to classic genetic manipulation,
as replication outside of a mammalian cell is not yet possible and,
despite advances in chlamydial transformation [32–34], routine
genetic manipulation has not yet been achieved. With these
limitations, genomic-scale approaches have been invaluable
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Results and Discussion
We synchronously infected HEp-2 cell monolayers with C.
trachomatis serovar E EBs (MOI,1). Replicate infections and
mock-infected controls were established. Infections and controls
were harvested at 1 and 24 hours post infection (hpi), encompassing infection, (1 hpi), differentiation and replication (24 hpi). Total
RNA was extracted and split into two fractions. Both were
subjected to simultaneous rRNA depletion; one fraction was
optionally subjected to poly(A) subtraction to further increase the
yield of bacterial mRNA (Figure 1). Depleted fractions were
combined and RNA-Seq libraries constructed. Reads from deep
sequencing were mapped to the human genome (release hg19) and
the C. trachomatis serovar E genome (Figure 1), yielding
,1.1 billion uniquely mapped Illumina HiSeq2000 sequence
reads (Table 1). Reciprocal mapping demonstrated that no reads
mapped to the other genome. Normalized RPKM (reads per
kilobase per million mapped reads) [17] values were determined
(Table S1) and the distribution plotted (Figure 2). To validate
RNA-Seq expression, we examined fifteen immediate-early
Chlamydia genes with a range of RPKMs by quantitative reverse
transcriptase PCR (qRT-PCR). A strong correlation was found
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RNA-Seq Profiling of Bacteria-Infected Cells
Figure 1. The simultaneous RNA-Seq pipeline. (a) Laboratory pipeline for simultaneous depletion of rRNA from prokaryotic and eukaryotic RNA
mixtures. The enriched mRNA is used to create RNA-Seq libraries. (b) Bioinformatics pipeline for sequential mapping and analysis of simultaneous
RNA-Seq data.
doi:10.1371/journal.pone.0080597.g001
infected cells; these uninfected cells will alter the expression profile
obtained, which is a summation of transcripts over the total cells
sampled. To alleviate this summation effect in future, we are
applying our simultaneous RNA-Seq method to single cells
infected with Chlamydia. Using higher MOIs to inflate the
pathogen transcript count will also distort expression profiles and
moves the already simplified in vitro infection model further away
from natural infections. We chose an MOI of 1 to ensure the
maximum numbers of cells were infected while minimizing
pathogen transcript inflation. With these limitations in mind, we
find that applying a stringent cutoff to Chlamydia sequence reads
from synchronized infections yields substantial insight into early
chlamydial transcription.
between normalized sequence coverage depth and qRT-PCR
transcript abundance at 1 hpi (R2 = 0.89; Figure S2), demonstrating that even with low numbers of infecting organisms, RNASeq on the infected cell detects real immediate-early chlamydial
gene expression.
Simultaneous transcriptional profiling by RNA-Seq
The technical potential of performing RNA-Seq on bacterial
pathogens and their host cells simultaneously, as realized here, was
recently assessed in a thought experiment [56]. One million nonrRNA bacterial reads and 100 million non-rRNA host cell reads
were estimated to be required, although no data was presented in
support [56]. These estimates do not account for different
pathogens, MOI or changes in pathogen numbers over the course
of infection. With the exception of Chlamydia reads at 1 hpi
(Table 1), we exceed these estimates for both Chlamydia and the
host cell. Obtaining as many sequence reads as possible is
obviously desirable, however a limiting number of available
organisms per cell restricts sequence yields at 1 hpi.
In this study, limiting numbers of Chlamydia at 1 hpi arises
primarily because replication has not commenced post-invasion
and also because we use an MOI of 1. Natural chlamydial
infections are likely to occur at a much lower MOI. However using
an MOI of less than 1 in in vitro infections will lead to fewer
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Chlamydial transcription at 1hpi
We first examined chlamydial gene expression at the immediate-early infection time (1 hpi). At this point of infection prior to
bacterial replication, we reasoned there would be few chlamydial
transcripts. More sequencing was performed (relative to 24 hpi) to
increase chlamydial transcript recovery. Poly(A) depletion on a
portion of each sample was also employed (Figure 1). As
expected, a low number of Chlamydia-specific transcripts were
found at 1 hpi (total of 131,892 reads), representing a small
percentage of total mapped reads (0.02%; Table 1).
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RNA-Seq Profiling of Bacteria-Infected Cells
1 hpi using a hundred-fold higher MOI [36], highlighting the
sensitivity and dynamic range of RNA-Seq. 24/29 microarraydetected genes are found at this cutoff. We next examined highly
expressed genes at 1 hpi. Highly expressed genes are defined by an
RPKM$1.0 and a minimum of 50 mapped reads (Table 2;
Table S2). With this stringent cutoff, 153612 chlamydial genes
(17.0% of all chlamydial genes) are highly expressed at 1hpi, again
contrasting with the previous finding of 29 genes by microarray
[36]. 20/29 microarray-detected genes are found at this cutoff.
The genes expressed at 1 hpi by microarray [36] but not found in
our RNA-Seq data at either cutoff are hypothetical genes or have
putative metabolic functions (Table S3). Their absence in our
data may arise from chlamydial strain or host cell variation, or
other experimental differences such as the high MOI (,100) used
in the original study [36]. We focus on the highly expressed subset
from this point forward.
We find immediate-early expression of the chlamydial general
secretory (Sec) pathway (SecD, E, F, G and Y). Fifty-one proteins
that form the proteinaceous components of the 30S and 50S
ribosomal subunits are highly transcribed at 1 hpi and 24 hpi
(Table S2). This supplies the core components of the chlamydial
ribosome, supporting previous observations of early chlamydial
protein synthesis [58,59]. Fifty-six highly expressed genes are
found at 1 hpi alone (Figure S1; Table S2). Known early genes
are within this subset, including the proposed master regulator euo
[60,61] and the secreted inclusion proteins incD, E, F and G
[36,62]. Many are novel at this immediate-early time. Twenty-five
of the unique 1 hpi highly expressed genes are hypothetical genes
(Table S2). Thirteen of these have not previously identified as
immediate-early genes, representing uncharacterized biological
functionality within this phase of infection. Gene Ontology (GO)
analysis of the remaining non-hypothetical genes reveals primarily
metabolic and catabolic functions including biosynthetic process,
transmembrane transport and carbohydrate metabolic process, consistent
with an auxotrophic bacterium establishing an infection within a
host cell (Table S4).
Acquisition of host cell nutrients is critical for chlamydial
survival; many of the up-regulated immediate-early genes are
directly relevant to this need. These include npt1 (CT065; ADP/
ATP translocase) and npt2 (CT495; nucleoside phosphate transporter), which enable parasitism of host energy and nucleotides
[36,63]. In addition, we identify numerous transferases, transporters, permeases, proteases and other factors putatively involved in
the interconversion or translocation of host metabolites (Table S2), consistent with the GO term enrichment analysis.
Several chlamydial genes predicted to encode riboflavin
biosynthetic enzymes (ribBA, ribC and ribH) are highly expressed
at 1 hpi (Table S2). Riboflavin biosynthesis is linked to iron
acquisition in several bacterial pathogens [64,65], where riboflavin
is an electron donor for the crucial reduction step of Fe3+ to Fe2+.
Iron is essential for chlamydial growth (reviewed by [31]), but how
iron is acquired from the host cell is unclear. Typical strategies of
iron acquisition do not apply as siderophore biosynthetic enzymes
or host iron-binding receptors are not present, although a
chlamydial metal ATP-binding-cassette (ABC) permease system
(ytgABCD) is implicated in iron transport and regulation [66,67].
ribBA encodes an bifunctional enzyme with GTP cyclohydrolase
and 3,4-dihydroxy-2-butanone-4-phosphate synthase (DHBP)
synthase activities. These catalyze the initial rate limiting steps of
the two riboflavin biosynthesis pathways [68,69]. Bifunctional
ribBA is found in Helicobacter pylori; the bifunctionality enables a
rapid co-regulated riboflavin biosynthetic response to iron-induced
stress [65]. Immediate-early expression of a bifunctional ribBA and
other riboflavin biosynthetic enzymes may be part of a chlamydial
strategy to rapidly obtain soluble iron from the host cell.
Chlamydial transcription at 24 hpi
In contrast to the low number of Chlamydia-specific reads at 1
hpi, over 18 million reads were uniquely mapped at 24 hpi
(Table 1). Chlamydia sequence reads at 24 hpi represent a higher
proportion of total mapped reads (28.4% versus 0.02% at 1 hpi;
Table 1). This is consistent with peak chlamydial gene expression
in the in vitro developmental cycle and highlights why this
timepoint has been well defined by previous microarray studies.
Using RPKM$0.1 and a minimum of 10 mapped reads, 80965
of 898 genes were detectably expressed by 24 hpi, representing
90.2% of the genome (Table 2; Table S2). As noted by Belland
et al [36], transcription of this number of chlamydial genes by this
stage of the in vitro lifecycle highlights the degree of optimization of
the reduced Chlamydia genome.
Using RPKM$1.0 and a minimum of 50 mapped reads,
22061 genes are highly expressed by 24 hpi (24.5%; Table 2;
Table S2). 109 are also highly expressed at 1 hpi (Figure S2;
Table S3). 112 highly expressed genes are found only at 24 hpi
(Fig S1; Table S2), including 34 hypothetical genes with no
known function. Together with genes expressed only at 1 hpi, this
confirms the broad pattern of temporal gene expression observed
by earlier microarray analyses [36–38]. Applying simultaneous
RNA-Seq to more infection timepoints should improve our
understanding of these temporal gene expression profiles. The
later timepoints of in vitro chlamydial infection such as 24 hpi are
better characterized, as the large amount of chlamydial transcripts
at these times falls well within the limits of microarray analysis.
Nevertheless, ahpC (CT603; thioredoxin peroxidase), trxA (CT539;
thioredoxin) and a predicted ferredoxin (CT312), all with putative
antioxidant properties, are expressed at 24 hpi but have not been
previously described. Host cells quickly produce reactive oxygen
species (ROS) on chlamydial infection [70]. Increased expression
of these genes by 24 hpi may be an expedient chlamydial response
to ROS bursts and oxidative stress.
Table 2. Number of chlamydial genes expressed at 1 and 24 hpi, by cutoff and by replicate (R).
Standard gene expression cutoff
‘‘Highly expressed’’ gene cutoff
.0.1 RPKM (10 reads minimum)
.1 RPKM (50 reads minimum)
R1
R2
R3
Avg (std dev)
R1
R2
R3
Avg (std dev)
1 hpi
205*
365
432
399 (34)
37*
141
165
153 (12)
24 hpi
815
802
811
809 (5)
219
221
219
220 (1)
*Excluded (insufficient reads).
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The effect of in vitro chlamydial infection on host cell
transcription
Many extracellular matrix components are differentially
expressed on chlamydial infection
Host cell transcription responding to infection was examined by
differential expression (DE) analysis of unique mapped reads from
infected HEp-2 cells and a time-matched mock-infected HEp-2
control (Table 1). Using a false discovery rate (FDR) cutoff of
#0.05 and log fold change (LFC) cutoff of $2.0, we identify 622
DE host transcripts at 1 hpi and 87 at 24 hpi (Table S5). 82 genes
are differentially expressed at both times, with 4 genes differentially expressed at 24 hpi alone (Figure S1), suggesting that the
establishment of infection has a greater effect on host cell
transcription. To validate RNA-Seq expression levels of host
genes, we selected twenty-four host genes (twelve each at 1 hpi and
24 hpi) with a range of RPKM values. A strong correlation was
found between RNA-Seq normalized sequence coverage depth
and qRT-PCR transcript abundance at both 1 and 24 hpi
(R2 = 0.61; Figure S4). Overrepresented Gene Ontology (GO)
terms for all DE host genes with annotation reveals a wide variety
of functions, including inflammatory response, immune response and antiapoptosis (Table S3), consistent with Chlamydia-induced immunopathologic processes.
The early host cell response to infection is not ‘‘quiescent’’ [54].
Some of the host cell responses observed may be elicited by
chlamydiae to promote survival and replication. However, the host
response measured here by comparing mock-infected cell lysates to
infected cell lysates, includes both specific reactions to Chlamydia
and non-specific cellular reactions to a phagocytosed foreign body.
The key events of chlamydial uptake and endosomal trafficking,
amongst others, may not be discernable from non-specific cellular
responses in this experimental design. RNA-Seq experiments using
opsonized latex beads and UV-killed EBs are in progress and
should permit differentiation of non-specific responses. With these
caveats, we still observe a diverse and dramatic host transcriptional
response to chlamydial infection that has not been previously
described. This encompasses many cellular pathways and functions, including growth factors, altered intercellular junctions and
adhesion, disruptions to Wnt and Notch signaling, extensive
cytoskeletal remodeling, lipid trafficking, transcriptional regulation
and non-coding RNA (Text S1).
Chlamydial disease is an adverse outcome of host inflammation
(reviewed by [71]). Repeated stimulation, from either long-term
infection or successive re-infections, leads to tissue damage and
scarring. There is evidence that Chlamydia exploits immune and
inflammatory pathways for survival [30,72,73]. Under the cellular
paradigm of chlamydial pathogenesis [74], infected epithelial cells
are the first responders to chlamydial infection, initiating and
promoting the immune response [30,72,73,75]. Subverting or
dampening this response may contribute to the adverse consequences of infection. We identify numerous host cell transcriptional responses to infection, including putative modulation of
innate responses through cytokines, chemokines, immune signaling molecules such as sphingosine 1-phosphate, semaphorins,
damage-associated molecular patterns (DAMPs), and the inflammasome, all of which can be interpreted in the context of immune
dampening (Text S1). We again note that these transcriptional
responses are derived from a highly simplified in vitro model of
chlamydial infection within epithelial cell monolayers. While this in
vitro model is widely used in the study of chlamydial biology,
natural infection is more complex and dynamic, with many
different cell types in tissues and the immune system interacting
with infected cells. Nevertheless, with this global perspective of the
in vitro host cell transcriptional response to infection, we identify a
subset of differentially expressed genes that may provide novel
insight into chlamydial scarring (Table S5).
The extracellular matrix (ECM) is the dynamic interdependent
network of proteins, proteoglycans and glycoproteins that
enmeshes epithelial cells and tissues [76]. The ECM has a major
role in cellular adhesion, patterning and architecture, and
‘‘outside-in’’ signal transduction [76]. In our RNA-Seq data,
many host ECM moieties are differentially expressed, supporting a
dramatic and rapid remodeling of the extracellular milieu in
response to chlamydial infection, including mucins (Text S1),
metalloproteinases, numerous collagens and several fibrosisassociated moieties.
Epithelial cells, PMNs and other immune cells produce
molecules that remodel the ECM, notably the zinc-dependent
matrix metalloproteinases (MMPs) [77–79]. MMPs influence cell
behavior by releasing growth factors and biologically active
peptides from the ECM and by regulating inflammatory mediators
[80]. The behavior of these molecules is linked to chlamydial
scarring. MMP-9 (gelatinase B) is implicated in infected murine
oviduct fibrosis [81]. Increased activity of MMP-9 was found in
human endothelial cells infected with C. pneumoniae [82] and in the
conjunctiva of trachoma patients [46]. MMP-2 (gelatinase A) and
MMP-9 were identified in infected human fallopian tube organ
cultures [83]. MMP2 and MMP9 activity induce tenascin-C
expression, which in turn induces further MMP expression,
creating a positive feedback loop of MMP/tenascin-C activity
that could contribute to chlamydial scarring (see below). We
observe up-regulation of MMP-2 at both 1 and 24 hpi. MMP-9
expression is not detected and may be specifically secreted by
PMNs or other immune cells rather than epithelial cells. Testican1/SPOCK1 (sparc/osteonectin, cwcv and kazal-like domains
proteoglycan 1), a highly conserved chimeric proteoglycan that
regulates MMP-2, is also up-regulated at 1 hpi [84]. Conversely
MMP-28 (epilysin), expressed in normal tissues [85] and thought to
participate in tissue homeostasis, is down-regulated in infected cells
at 1hpi. Membrane-bound MMP-24 is down-regulated at 1hpi.
In addition to these MMP expression patterns, we find
differential expression of other proteinases that are previously
unreported in the context of chlamydial infection. Six members of
the ADAM (A Disintegrin And Metalloproteinase) and ADAMTS
(A Disintegrin And Metalloproteinase with ThromboSpondin
motifs) [86,87] families of proteinases exhibit differential expression at both 1 and 24 hpi. The membrane-bound ADAM proteins
activate zymogens such as TNF-a, and participate in cell adhesion
via integrin interaction [86,87]. ADAM proteins also participate in
activation of the conserved Notch signaling pathways (see Text S1)
[88]. ADAMTS are secreted proteins that modulate the ECM by
cleavage of procollagen and proteoglycans [86]; these fragments
may act as ligands for further inflammatory signaling. ADAM33
and ADAMTSL4 are down-regulated at 1 hpi, whereas ADAM12,
ADAM19, ADAMTS3 and ADAMTS6 are up-regulated at both 1
and 24 hpi.
Collagens are a major component of ECM scaffolding,
conferring tensile strength and viscoelasticity [89]; collagens also
interact with integrins and other signaling receptors [90].
Immunohistochemical examination of conjunctiva from patients
with active trachoma previously showed new (type V) and
increased (type I, III and IV) collagen deposition [91]. Remarkably, eight members of the collagen superfamily are differentially
expressed in our data, indicating that collagen deposition processes
are initiated very early in infection rather than as a late
consequence of disease progression. COL3A1, COL4A1, COL4A2,
COL5A1, COL5A3, and COL16A1 are all up-regulated at 1 hpi
only; COL25A1 is up-regulated at both 1 and 24hpi. COL15A1 is
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down-regulated at 1 hpi. Collagens are subdivided based upon
their supramolecular assemblies: COL3A1, COL5A1 and COL5A3
are fibril-forming; COL4A1 and COL4A2 are network-forming;
COL15A1 is a multiplexin, containing multiple triple-helix
domains (collagenous domains) interrupted by non-collagenous
domains; COL16A1 is fibril-associated; and COL25A1 is membrane-associated [89]. The type IV collagens, including COL4A1
and COL4A2, are part of the basement membrane, an ECM layer
that coats the basal aspect of epithelial cells [89].
Several other basement membrane components [92] are
differentially expressed early in infection, notably subunits of the
laminin heterotrimer (LAMA4 and LAMC3), and nidogen (NID1)
which crosslinks collagen IV and laminin. LAMA4 and NID1 are
strongly up-regulated at 1hpi, whereas LAMC3 is down-regulated.
LAMA4 is also strongly up-regulated at 24 hpi. Fibulin-5 (FBLN5)
and hemicentin (HMCN1), both secreted glycoproteins that
interact and crosslink with other members of the ECM [93] are
strongly up-regulated. These dramatic early expression changes of
numerous ECM moieties may underlie chlamydial disease
outcomes through fibrotic scar formation.
lesions in cervical epithelia [95,96]. Moreover, TNC is associated
with inflammatory processes, including TLR4 induction of proinflammatory cytokines, re-epithelialization, and tissue remodeling
[95,96,98]. During acute inflammatory events, TNC expression is
concentrated in regions of increased immune cell infiltration, and
is particularly associated with PMN infiltration [96]. PMN
recruitment is a well-known feature of the immune response to
chlamydial infection [71].
TNC expression is linked to TGF-b-mediated fibrosis and
induces TGF-b expression [95,96,99,100]. A central role for TGFb in chlamydial disease outcomes has been previously discussed
(reviewed by [101]). Increased expression of TNC on chlamydial
infection may create a positive feedback loop that ultimately results
in increasing amounts of collagen and other ECM components
being deposited. We find strongly increased expression of TGF-b2
at 1 hpi; increased expression of multiple collagen family members
was noted above. A central role for TGF-b-mediated fibrosis is
further supported by potential microRNA-mediated alterations of
TGF-b expression in trachoma patients [102]. Another positive
feedback loop has been posited for the interaction between TNC
and MMPs induced by inflammation (see above) [96]. Fragments
of collagen and other ECM components produced by MMPs will
further stimulate inflammation [80]. In addition, Wnt signaling
pathways intersect with TGF-b-mediated fibrosis [103]; we
observe differential expression of several components of Wnt
signaling (Text S1).
Thus, we find transcriptional evidence of several paracrine
responses to early chlamydial infection that intersect with TNC
and TGF-b, and which may induce scarring through uncontrolled
positive feedback loops. As an aside, persistent TNC expression has
also been linked to atherosclerosis, where it contributes to both
plaque formation and rupture [95,98]. In this study, we used a
genital serovar of C. trachomatis, however, C. pneumoniae is
controversially linked to several chronic conditions with inflammatory etiologies, including atherosclerosis. Chlamydial dysregulation of TNC and other positive feedback loop participants may
provide an insight into the correlation of C. pneumoniae with
atherosclerosis and other multifactorial inflammatory diseases.
Building on this theme of dysregulated cellular processes
contributing to fibrosis, at both 1 and 24 hpi we also observe
strongly increased expression of gremlin (GREM1), an antagonist
of bone morphogenetic protein (BMP) receptors [104]. Upregulation of GREM1 at both 1 and 24 hpi was confirmed by qRTPCR (Figure 3). BMPs are members of the TGF-b receptor
superfamily and are key participants in tissue remodeling [105].
GREM1 is a cysteine knot-secreted protein that is implicated in
the prevention of epithelial regeneration and participates in the
epithelial-to-mesenchymal transition that converts epithelial cells
to fibrotic myofibroblasts [104]. Transient overexpression of
GREM1 in rat lungs induces epithelial injury and reversible
pulmonary fibrosis [106]. GREM1 overexpression has been
identified as a direct inducer of fibrosis in diseases with fibrotic
etiologies, including asbestosis, pulmonary sarcoidosis, idiopathic
pulmonary fibrosis, glomerulonephritis, cirrhosis, and hepatic
fibrosis [104,107]. In addition, GREM1 overexpression will elicit
TGF-b-induced fibrosis in the lung; in turn, TGF-b itself induces
GREM1 production [107]. This suggests another positive
feedback loop, again centered on TGF-b and its ligands, that
may influence chlamydial scarring sequelae.
In summary, validated RNA-Seq analyses of Chlamydia-infected
epithelial cells demonstrate remarkable early increased expression
of host genes directly associated with fibrosis and collagenous
scarring. Combined with increased expression of fibril- and
network-forming collagens and other ECM constituents, this is
Hypothesis – mechanisms for chlamydia-induced fibrotic
scarring
Long-term chlamydial infection (or re-infection) that is untreated or undetected will produce disease sequelae arising from
collagenous scar formation on mucosal surfaces. For trachoma,
scarring of the conjunctiva causes the eyelid to roll inwards by scar
contraction (entropion). The eyelashes subsequently abrade the
cornea (trichiasis), resulting in corneal opacity and blindness [94].
In genital infections of women, pelvic inflammatory disease and
ascending infection precede scar formation in fallopian tubes,
leading to tubal infertility, hydrosalpinx or ectopic pregnancy [30].
The molecular processes that lead to these adverse outcomes are
largely unknown. Combined with the transcriptional changes in
ECM components described above, we identify possible positive
feedback mechanisms for chlamydiae-induced fibrotic scarring
that center upon tenascin C, gremlin1 and TGF-b.
Two members of the tenascin ECM glycoprotein family are
notably differentially expressed upon chlamydial infection relative
to mock-infected cells: tenascin-C (TNC) is up-regulated, whereas
tenascin-X (TNXB) is down-regulated. Upregulation of TNC on
chlamydial infection at both 1 and 24 hpi was confirmed by qRTPCR (Figure 3). TNC is a pleiotropic protein with multiple
binding domains, including EGF and fibronectin repeats that are
subject to alternative splicing. It has numerous potential glycosylation sites, creating the potential for many isoforms [95,96]. TNC
has a hexameric protein organization that may enable extensive
cross-linking. It promiscuously interacts with many ECM architectural molecules and receptors, and thus participates in both
structural and signaling processes [95,96].
TNC is not found in healthy tissues but is transiently expressed
on cellular injury, and mediates global fibrotic processes as part of
tissue repair [96]. After repair is complete, TNC expression
normally decreases. However, abnormal persistent TNC expression is correlated with excessive matrix deposition that leads to
collagenous scar formation in several fibrotic diseases [96] – this
suggests an equivalent role in chlamydial scarring. Production of a
tenascin protein has been previously observed by immunohistochemical studies of conjunctival biopsies taken from patients with
trachomatous conjunctivitis [97]. Higher expression is also found
in chronic cardiac conditions, and is a reliable indicator of poor
patient prognosis [95,96]. Abnormal TNC expression also drives
matrix degradation in arthritic diseases, and fibrosis in response to
infections, including lung damage from tuberculosis and HPV
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8.
RNA-Seq Profiling of Bacteria-Infected Cells
Figure 3. Confirmation of differential expression for selected pro-fibrotic genes over time. (a) Gremlin1 and (b) tenascin-C in Chlamydiainfected cells at 1 and 24 hpi, compared to mock-infected cells. Values are based on fold changes calculated from absolute quantitation of each gene
of interest, normalized to human ATP synthase 6. Asterisks indicate statistically significant differences as calculated by Student’s t test (***: p,0.0001;
**: p,0.002). Error bars represent standard deviation over a minimum of 2 biological replicates.
doi:10.1371/journal.pone.0080597.g003
The epithelial host cell response to chlamydial infection in vitro is
rapid and dramatic. A central paradox of chlamydial infection is
that the immune response contributes to disease pathology. We
find transcriptional evidence, within the constraints of a simplified
model of infection, for attempted immune dampening through
alterations in antimicrobial peptide and mucin expression, by
mitigation of innate immunity and potential interference with
signaling pathways, and by possible differential recruitment or
repulsion of immune cell subsets. We identified and validated
abnormal early transcription of host factors linked to scarring in
numerous other fibrotic conditions. Chlamydia-induced aberrant
expression of these factors may induce positive feedback loops that
amplify tissue damage. Continuing reinforcement of these
feedback loops may also provide an explanation for disease
severity from long-term infection or re-infection.
Remarkably, transcription of these putative immune dampening
and tissue damaging factors are evident as early as 1 hpi. Thus,
depending on host factors influencing immune dampening or the
severity of the fibrotic response, we speculate that the initial
infection insult could be sufficient to commit a host into the
responses that ultimately result in scarring. Subtle alterations of
such a multidimensional equilibrium between the host cell and the
invader may permit pathogen clearance on the one hand, or
enable ongoing cryptic infection (or reinfection) with the resulting
scarring sequelae. Genotypic variability of both infected individuals and chlamydial strains are likely to be major factors governing
these equilibrium states.
We have focused on Chlamydia-infected cancerous epithelial
cells in vitro. Natural chlamydial infection in vivo occurs with fewer
organisms and fewer infected cells in a complex and dynamic
host environment, often with other bacterial species in close
proximity. With appropriate sequencing depth, simultaneous
transcriptional profiling by RNA-Seq could be used to examine
Chlamydia and infected primary host cells from ex vivo human
tissue or in vivo animal models, and ideally, single infected cells.
Beyond Chlamydia, this approach is applicable to any bacteria (or
bacterial community) that interact with eukaryotic cells, encompassing parasitic, commensal or mutualistic lifestyles. Using
simultaneous RNA-Seq to compare experimental or environ-
relevant to the long term scarring sequelae of chlamydial disease.
From these findings, we hypothesize that dysregulated early
persistent expression of at least TNC, GREM1, TGF-b2 and
various proteases in infected epithelial cells mediates Chlamydiainduced tissue damage.
We further hypothesize that in susceptible individuals, these
dysregulated genes establish a series of interlocked positive
feedback loops that disrupt the homeostasis of multiple pathways,
ultimately resulting in increased deposition of collagen and other
ECM constituents (Figure 4). Ongoing inflammatory stimulation
by lengthy infection or re-infection and recruitment of immune
cells that participate in the fibrotic response, such as collagendepositing fibroblasts, could exacerbate scarring through positive
feedback loop reinforcement. As not all individuals develop
scarring sequelae, host factors are likely to influence disease
outcomes [30,71]. We postulate that these susceptibility differences may be mediated by host genetic variation that influences
the impact of these feedback loops by increased degree and/or
period of aberrant expression, increasing collagen deposition
rates and propensity for scarring. These hypotheses are currently
being tested in both in vitro and in vivo systems that better capture
the complexity of natural chlamydial infection.
Conclusions
We developed and applied the simultaneous RNA-Seq method,
using Chlamydia-infected cells as proof of principle. Despite a low
MOI and substantial amounts of eukaryotic RNA, our method
readily distinguishes chlamydial and host expression, yielding a
detailed view of both host and pathogen transcription particularly
in the poorly characterized early stages of infection. A substantial
transcriptional program is rapidly initiated by Chlamydia following
adherence and uptake, including carry-over transcripts from the
infecting EBs and new transcription. In addition to the core
components of the chlamydial ribosome, the Sec pathway and
numerous novel hypothetical genes, we identify early expression of
a bifunctional riboflavin biosynthetic enzyme that may mediate
soluble iron acquisition from the host cell.
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9.
RNA-Seq Profiling of Bacteria-Infected Cells
Figure 4. A proposed model of chlamydial-induced fibrosis and chronic scarring through the induction of multiple positive
feedback loops. Infection of epithelial cells by Chlamydia leads to production of proinflammatory cytokines and chemokines that lead to
recruitment and activation of immune cells. Recruited immune cells and infected epithelial cells secrete pro-fibrotic matrix metalloproteases (MMPs)
that act upon the extracellular matrix (ECM), including collagens. The breakdown products of these proteases are also pro-inflammatory. Infected
epithelial cells express the pro-fibrotic molecules TGF-b, Gremlin1 and Tenascin-C; expression of each amplifies the other, creating a series of nested
positive feedback loops that increase the deposition of collagens and other ECM components, which in turn further induce immune cell recruitment
and activation.
doi:10.1371/journal.pone.0080597.g004
described [108], using centrifugation to synchronize infections.
Infections and subsequent culture were performed in the absence
of cycloheximide or DEAE dextran. A matching number of
HEp-2 monolayers were also mock-infected using uninfected cell
lysates. Each treatment was incubated at 25uC for 2 h and
subsequently washed twice with SPG to remove dead or nonviable EBs. 10 mL fresh medium (DMEM+10% FBS, 25 mg/ml
gentamycin, 1.25 mg/ml Fungizone) was added and cell monolayers incubated at 37uC with 5% CO2. Three infected and
mock-infected dishes per timepoint were harvested post-infection
by scraping and resuspending in 150 mL sterile PBS. Resuspended samples were stored at 280uC.
mental conditions, such as different wild-type or recombinant
pathogens infecting the same cell type, or the same strain
infecting a variety of cell lines or knockouts will give significant
insight into bacterial virulence factors and the dynamic host
response.
Methods
Preparation of C. trachomatis EBs and mock lysates
Monolayers of HEp-2 cells were infected with C. trachomatis
serovar E in SPG as previously described [108]. Additional
monolayers were mock-infected with SPG only. The infection
was allowed to proceed 48 hours prior to EB harvest, as
previously described [108]. C. trachomatis EBs and mock-infected
cell lysates were subsequently used to infect fresh HEp-2
monolayers.
RNA purification
Total RNA was purified from frozen HEp-2/C. trachomatis
lysates using the MasterPure RNA Purification kit (Epicentre, Cat.
No. MCR85102). Carryover DNA was treated twice with Turbo
DNA-free DNase (Ambion, Cat. No. AM1907), according to the
manufacturer’s protocol for rigorous sample treatment. Total
genomic DNA removal was verified by qPCR.
Infection time course
HEp-2 cells (American Type Culture Collection, ATCC No.
CCL-23) were grown as monolayers in 66100 mm TC dishes
until 90% confluent. Monolayers were infected with C. trachomatis
serovar E in 3.5 mL SPG buffer for an MOI,1 as previously
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RNA-Seq Profiling of Bacteria-Infected Cells
segment length = 30; maximum multi-hits per read = 25;
maximum intron length = 50000). Reciprocal mappings were
also performed to check that Chlamydia reads did not map to the
human genome (and vice versa). Mapped RNA-Seq reads were
visualized using the Integrative Genomics Viewer [111]. For
human reads, the number of reads mapped to each gene was
counted by HTSeq (http://www-huber.embl.de/users/anders/
HTSeq/) against gene annotation file for build GRCh37/hg19
from Ensembl (http://www.ensembl.org). Read count was used to
represent gene expression level. Data normalization and differential expression (DE) analysis were done using the methods
implemented in DESeq R package [112]. Briefly, read counts of
samples were normalized for sequencing depth and distortion
caused by highly differentially expressed genes. A negative
binomial (NB) model was used to test the significance of
differential expression between two conditions. A cutoff FDR
(False Discovery Rate) of less than 0.05 and a log fold change .2.0
was used to select significant DE genes. For C. trachomatis reads,
RPKMs of features for each sample were divided by their 75th
percentile and log2 transformed. GO enrichment analysis for
human DE genes was performed using the goseq R package [113],
normalizing for gene length bias. A cutoff of FDR less than 0.1 was
used to select significantly enriched GO categories.
For both human and C. trachomatis genes, Gene Ontology
annotations and associated data were extracted and arranged into
a tab-delimited file corresponding to the GO annotation file (GAF)
2.0 format (http://www.geneontology.org/GO.format.gaf-2_0.
shtml#fields). An in-house custom GO ontology was used for C.
trachomatis. For human, the current GO ontology and generic slim,
a subset of the ontology that contains selected high-level terms,
were downloaded from http://www.geneontology.org (April
2012). The Perl module map2slim, which maps a gene association
file containing annotations to the full GO to terms in a slim, was
downloaded, installed and run with the ‘‘-c’’ and ‘‘-t’’ options to
generate a count of the number of distinct gene products that
either are directly associated to a given slim term or would be
associated to a child of this term in the full ontology (http://
search.cpan.org/,cmungall/go-perl/scripts/map2slim). Any GO
slim terms with zero associations were removed from the resulting
table.
Simultaneous Eukaryotic and Prokaryotic Ribosomal RNA
depletion
Human and Gram-negative bacterial ribosomal RNA were
depleted from each sample using Ribo-Zero rRNA Removal
(Human/Mouse/Rat and Gram-negative) kits. An equivalent
volume of the Ribo-Zero beads from each kit was combined,
allowing removal of both human and bacterial rRNA simultaneously. The remainder of the protocol was followed as per the
manufacturers instructions. After rRNA reduction, each sample
was optionally split and one half subjected to poly-A depletion by
the Poly(A)Purist Mag purification kit (Ambion) to further enrich
bacterial transcripts. Briefly, poly(A) tailed mRNAs were bound to
magnetic beads and removed from solution using a magnet.
Poly(A)-depleted and rRNA-depleted eluates were further purified
using Zymo-Spin IC columns (Zymo Research) before being
combined for library construction. 1 mL of each final RNA eluate
was assayed with a RNA Nano chip on an Agilent BioAnalyzer
(Agilent Technologies) prior to RNA-Seq library construction and
sequencing.
Quantitative PCR
Complete DNA removal was verified by Taqman (Applied
Biosystems) assays for human beta-actin, ATP synthase 6, and 18S
rRNA genes. Each assay was performed on an ABI 7900HT
instrument according to the manufacturer’s instructions (Applied
Biosystems). Primers and probes were selected for C. trachomatis
genes and human genes using PrimerExpress (Applied Biosystems).
Assays were performed on an ABI 7900HT instrument according
to the manufacturer’s instructions for gene expression assays
(Applied Biosystems). C. trachomatis and human gene expression
values were normalized against 16S or 18S rRNA copy number as
appropriate. Primer and probe sequences are listed in Table S6.
Sequencing
Illumina mRNA-Seq libraries were prepared from rRNAdepleted samples using the TruSeq RNA Sample Prep kit
(Illumina, San Diego, CA) per the manufacturer’s protocol with
IGS-specific optimizations. Adapters containing 6 nucleotide
indexes were ligated to the double-stranded cDNA. The DNA
was purified with AMPure XT beads (Beckman Coulter
Genomics, Danvers, MA) between enzymatic reactions and size
selection steps (,250 to 300 bp). Libraries were initially sequenced
using the Illumina MiSeq sequencer for quality control. MiSeq
sequencing results were used to estimate sequencing depth from
HiSeq2000 sequencing. Libraries were subsequently sequenced
using the 100 bp paired-end protocol on an Illumina HiSeq2000
sequencer. Raw data was processed using Illumina’s RTA and
CASAVA pipeline software, which includes image analysis, base
calling, sequence quality scoring, and index demultiplexing.
FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/
) and in-house pipelines were used for sequence assessment and
quality control. These pipelines report numerous quality metrics
and perform a Megablast-based contamination screen. By default,
our quality control pipeline assesses basecall quality and truncates
reads where the median Phred-like quality score falls below Q20.
Supporting Information
Figure S1 Gene distributions between timepoints. (a) Unique
and shared chlamydial genes highly expressed (RPKM$1.0 and a
minimum of 50 mapped reads) at 1hpi and 24hpi. (b) Unique and
shared differentially expressed (FDR#0.05 and LFC$2.0) human
genes at 1 and 24 hpi.
(PDF)
Figure S2 Expression levels of selected Chlamydia genes compared to matching RPKM values. Total RNA was prepared from
biological duplicate infections (MOI = 1) at 1 hpi. Quantitative
RT-PCR assays were performed on 15 Chlamydia genes. Taqman
assays were designed for genes CT81, CT500, CT229, CT875,
CT734, CT446, CT577, CT18, CT864, CT665, CT834, CT391,
CT216, CT705, and CT416. Chlamydial gene expression is
plotted against the RPKM for each gene. qRT-PCR data is
expressed as 1/log2 Ct and normalized to 16S rRNA.
(PDF)
Bioinformatic analyses
Sequence reads were first mapped to the C. trachomatis D
(NC_000117.1) reference genome using Bowtie v 0.12.7 [109]
(maximum number of mismatches = 2; number of alignments
permitted per read = 1). The remaining sequence reads were
aligned to the human (hg19) reference genome using TopHat
version 1.3.2 [110] (maximum number of mismatches = 2;
PLOS ONE | www.plosone.org
Figure S3 Expression of 11 C. trachomatis E genes from 0 to 16
hpi as detected by qRT- PCR. Infections were performed using C.
trachomatis (MOI = 1) harvested at 1, 2, 4, 8, and 16 hpi and
assayed using gene-specific Taqman primer/probe assays. RNA
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